Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

Abstract

AbstractThe purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n=10 ActiGraph wGT3X-BT, n=10 Apple Watch Series 7, n=10 Garmin Vivoactive 4S, and n=10 Fitbit Sense) were compared to the criterion accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). For reliability testing, identical devices were oscillated at 0.6 and 3.2 Hz for 5 trials that lasted 2 minutes each. For validity testing, devices were oscillated for 1 trial across 7 speeds that lasted 2 minutes each. The intraclass correlation coefficient (ICC) was calculated to test inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, and mean bias were calculated to assess the validity between the raw estimates from the devices and the criterion metric. Estimates produced by the raw accelerometry data from Apple and ActiGraph were more reliable ICCs=0.99 and 0.97 than Garmin and Fitbit ICCs=0.88 and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the criterion CCCs=0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC=0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors=16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error=32.5mg compared to the criterion based on the mean aggregation method. ActiGraph produced the lowest mean bias 0.0mg (95%CI=-40.0, 41.0) based on the mean aggregation method. Raw accelerometry data collected from Apple and Fitbit are comparable to ActiGraph. However, raw accelerometry data from Garmin appears to be different. Future studies may be able to develop algorithms using device-agnostic methods for estimating physical activity from consumer wearables.